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Volume 31, Number 4—April 2025
Research

Attribution of Salmonella enterica to Food Sources by Using Whole-Genome Sequencing Data

Erica Billig Rose1, Molly K. Steele1Comments to Author , Beth Tolar, James Pettengill, Michael Batz, Michael Bazaco, Berhanu Tameru, Zhaohui Cui, Rebecca L. Lindsey, Mustafa Simmons, Jess Chen, Drew Posny, Heather Carleton, and Beau B. Bruce
Author affiliation: Centers for Disease Control and Prevention Atlanta, Georgia, USA (E.B. Rose, M.K. Steele, B. Tolar, Z. Cui, R.L. Lindsey, J. Chen, H. Carleton, B.B. Bruce); US Food and Drug Administration, College Park, Maryland, USA (J. Pettengill, M. Batz, M. Bazaco); US Department of Agriculture, Washington, DC, USA (B. Tameru, M. Simmons, D. Posny)

Main Article

Figure 2

Confusion matrix from the random forest model trained on Salmonella isolates collected from single food categories in the United States and other countries during 2003–2018 and 613 isolates collected before 2003. A) Confusion matrix for all Salmonella isolates from single food categories (N = 18,661). B) Confusion matrix from the random forest model for Salmonella isolates from single food categories with a maximum predictive probability of >0.50 (n = 14,888).

Figure 2. Confusion matrix from the random forest model trained on Salmonella isolates collected from single food categories in the United States and other countries during 2003–2018 and 613 isolates collected before 2003. A) Confusion matrix for all Salmonella isolates from single food categories (N = 18,661). B) Confusion matrix from the random forest model for Salmonella isolates from single food categories with a maximum predictive probability of >0.50 (n = 14,888).

Main Article

1These first authors contributed equally to this article.

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Page updated: March 24, 2025
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